Artificial Intelligence Products Research Articles

Is Copyright Law the New Turing Test?

In December 2023, the New York Times Company filed a lawsuit against OpenAI and Microsoft for the "defendants' unlawful use of The Time's work to create artificial intelligence products..." [8]. While this lawsuit alone should interest the Computers and Society community, it struck me that its eventual judgment might serve as an interesting exhibit as to whether computers are creative in their intelligent thinking? 1 This Parting Opinion focuses on this question. As usual, I'll present some background context to set the stage , before diving into the primary question at hand. As a bit of nostalgia, let's begin with the early days of Artificial Intelligence (AI) and quickly move to the present.

A Study Regarding Early Teacher'S Perception Toward the Introduction of Artificial Intelligence Applications and the Use of Educational Robots in the Teaching Process

Over the years Artificial Intelligence (AI) has revolutionised not only industries but also education worldwide. With the 2020 pandemic, teachers around the world have had to reconfigure their teaching process, so AI products are becoming more and more prevalent in early childhood settings to enhance the learning and development of pre-schoolers. Although robotic toys (Bee-Bot, Robot Mouse, Robotbloq Qobo, Ozobot) have been used for several years as modern teaching tools, teachers in Romania face some difficulties in integrating them into the teaching process, either because of lack of adequate training in handling these tools or because of the infrastructure needed for this purpose. The use of robotic toys in kindergarten activities is a matter of teachers' perceptions, so they decide on the use of robotic toys in the teaching process and the types of activities that would allow their use for teaching purposes. In our study, we aimed to measure teachers' perceptions of children's use of robotic toys and their incidence of using them appropriately, in the teaching process, integrated into Experiential Domain Activities. In this way, we will be able to make correlations between the use of these tools and teachers' perceptions, highlighting the benefits they bring to the teaching process and consequently to the development of children in early education.

Risks of the Malicious Use of Artificial Intelligence and the Possibility of Minimizing Them

A rapid improvement of intellectual systems capable of performing creative functions, which was in the past viewed as a unique human ability, has been a breakthrough in the technology progress of recent years. As access to it became available for a wide range of people, the popularity of artificial intelligence (AI) in various spheres of human activities has rocketed. This phenomenon, however, is fraught with negative consequences — the use of AI systems for criminal purposes. The authors describe the character of such threats, and review the results of their analysis by foreign researchers, as this problem emerged abroad somewhat earlier than in our country. The application of international experience of counteracting the criminal use of artificial intelligence while taking into consideration national specifics is also useful from the standpoint of predicting further dynamics of the situation and reducing its negative consequences. The authors discuss such problems associated with the use of AI systems as the violation of copyright in generating images, the creation speech and video deepfakes for distance fraud and extortion, the dissemination of pornography, and discrediting people. The growing public danger of harassment and humiliation in AI-assisted cyberbullying is stressed. The case when a West European person committed suicide after communication with a chat bot of an AI system is highlighted, together with the fact that the bots’ creators were not prosecuted. Discussions on determining the subject of crimes in cases of accidents involving UAVs are studied, dangerous trends in the use of AI products in the military sphere are identified, and the necessity of prohibiting “autonomous weapons” at the international level is stressed. The authors analyze first attempts at the legal regulation of the use of AI products and propose the following measures of preventing, neutralizing and reducing the risks of AI systems’ malicious use: user verification, labeling of AI-created products, testing new systems for possible criminal use, quick reaction to criminal incidents by working out recommendations for their prevention, improvement of the systems to identify a covert use of AI, legislative changes that take into account new facts of publicly dangerous actions, limitation of the use of AI instruments in specific spheres (military, jurisprudence, expert work) and determination of the possibilities to use them for completing creative qualifying work, competition tasks, dissertations.

Why Do Older Adults Feel Negatively about Artificial Intelligence Products? An Empirical Study Based on the Perspectives of Mismatches

Artificial intelligence products (AIPs) for older adults enhance the functions of traditional products and improve the quality of their lives. However, AIPs are not popular among this population, and limited attempts have been made to investigate these users’ negative tendencies regarding AIPs. This study explores the causes of avoidance and exit behaviors toward AIPs among older people from both a functional and socio-emotional mismatch perspective. Data were collected from 1102 older AIP users to verify the research model and hypotheses. The results indicate that perceived control and expectation disconfirmation affect the functional mismatch, while public stigma has the greatest impact on the socio-emotional mismatch. Furthermore, the results highlight a mixed influence of the functional and socio-emotional mismatches on negative behaviors. This study explores older people’s negative tendencies toward AIPs, comprehensively considering the functions of AIPs and the socio-emotions they evoke. Thus, it provides new empirical evidence for the systematic relationship between the functional mismatch and the socio-emotional mismatch and fills the research gap on the influence on the subsequent behaviors of older adults. Additionally, this study sheds light on the specific methods of designing, developing, and promoting AIPs.

Impact of Trust Adjustment Factor on Artificial Intelligence Driven Collaborative Filtering Recommendation Algorithm

Trust on artificial intelligence (AI) is a major concern in the contemporary computing paradigms. Studies show that AI systems may outsmart humans, leading to an ultimate extinction of mankind. Therefore, the behavior of these systems must be controlled to avert potential use by bad actors. Recommender systems, which are variant of AI products, learn shoppers past data and predict items that shoppers may prefer. This helps in identifying items that may be recommended to the active user. Studies indicate that classical recommender systems allow untrustworthy data, tempting unscrupulous dealers to misdirect the learning process. This action potentially defrauds buyers. Our study introduces trust adjustment factor into the AI learning pipeline. We conducted experiments to test the difference in robustness of the trust-enhanced collaborative filtering recommendation algorithm against the classical counterpart. Prediction shift and hit ratios for the two sets of algorithms were measured when subjected to various forms of profile injection attacks. We found that the trust-enhanced variant of the algorithm significantly outperforms classical collaborative filtering recommendation in terms of robustness by up to 52% when measured by prediction shift and by up to 18% when measured by hit ratio. Confirmed by t-test, results suggest that embedding trust adjustment factor into recommender systems improves its robustness.

The Transformation of English Interdisciplinary Education in the Context of Artificial Intelligence

Currently, artificial intelligence products represented by ChatGPT are having a significant and far-reaching impact on school education. However, there are many education practitioners who are concerned about their negative impact on the education industry, and currently there are education departments and colleges and universities that have introduced a ban on the use of ChatGPT. However, it is better to block than to block, and how to deal with it properly should be the focus of teaching administrators and related organizations. For English education, we should not only teach students to use English to communicate, but also focus on cultivating students' national emotions and family values, so that they can grow up to be internationalized talents with both integrity and talent. English interdisciplinary theme learning as a new way of learning, in theory, it can provide path support for the implementation of students' core literacy through the cultivation of students' problem solving ability, but in practice, it is inevitable to encounter the problem that the English level of our primary and secondary school students is much lower than the cognitive level, how to grasp the opportunity of the times, the use of AI to empower English education, and to promote the transformation and upgrading of the teaching mode, it is worth exploring and thinking about. It is worthwhile for us to explore and think.

The impact of human-AI collaboration types on consumer evaluation and usage intention: a perspective of responsibility attribution.

Despite the widespread availability of artificial intelligence (AI) products and services, consumer evaluations and adoption intentions have not met expectations. Existing research mainly focuses on AI's instrumental attributes from the consumer perspective, along with negative impacts of AI failures on evaluations and willingness to use. However, research is lacking on AI as a collaborative agent, investigating the impact of human-AI collaboration on AI acceptance under different outcome expectations. This study examines the interactive effects of human-AI collaboration types (AI-dominant vs. AI-assisted) and outcome expectations (positive vs. negative) on AI product evaluations and usage willingness, along with the underlying mechanisms, from a human-AI relationship perspective. It also investigates the moderating role of algorithm transparency in these effects. Using three online experiments with analysis of variance and bootstrap methods, the study validates these interactive mechanisms, revealing the mediating role of attribution and moderating role of algorithm transparency. Experiment 1 confirms the interactive effects of human-AI collaboration types and outcome expectations on consumer evaluations and usage willingness. Under positive outcome expectations, consumers evaluate and express willingness to use AI-dominant intelligent vehicles with autonomous driving capabilities higher than those with emergency evasion capabilities (AI-assisted). However, under negative outcome expectations, consumers rate autonomous driving capabilities lower compared to emergency evasion capabilities. Experiment 2 examines the mediating role of attribution through ChatGPT's dominant or assisting role under different outcome expectations. Experiment 3 uses a clinical decision-making system to study algorithm transparency's moderating role, showing higher transparency improves evaluations and willingness to use AI products and services under negative outcome expectations. Theoretically, this study advances consumer behavior research by exploring the human-AI relationship within artificial intelligence, enhancing understanding of consumer acceptance variations. Practically, it offers insights for better integrating AI products and services into the market.

Projected Growth in FDA-Approved Artificial Intelligence Products Given Venture Capital Funding

PurposeMedical imaging accounts for 85% of digital health’s venture capital funding. As funding grows, it is expected that artificial intelligence (AI) products will increase commensurately. The study’s objective is to project the number of new AI products given the statistical association between historical funding and FDA-approved AI products. MethodsThe study used data from the ACR Data Science Institute and for the number of FDA-approved AI products (2008-2022) and data from Rock Health for AI funding (2013-2022). Employing a 6-year lag between funding and product approved, we used linear regression to estimate the association between new products approved in a certain year, based on the lagged funding (ie, product-year funding). Using this statistical relationship, we forecasted the number of new FDA-approved products. ResultsThe results show that there are 11.33 (95% confidence interval: 7.03-15.64) new AI products for every $1 billion in funding assuming a 6-year lag between funding and product approval. In 2022 there were 69 new FDA-approved products associated with $4.8 billion in funding. In 2035, product-year funding is projected to reach $30.8 billion, resulting in 350 new products that year. ConclusionsFDA-approved AI products are expected to grow from 69 in 2022 to 350 in 2035 given the expected funding growth in the coming years. AI is likely to change the practice of diagnostic radiology as new products are developed and integrated into practice. As more AI products are integrated, it may incentivize increased investment for future AI products.

The effect of medical artificial intelligence innovation locus on consumer adoption of new products

The purpose of this study is to explore the effect of innovation locus on consumers' adoption of artificial intelligence products in healthcare. Across four experiments, we demonstrate that consumers are more likely to accept medical artificial intelligence for peripheral products than for core products. This effect is mediated by perceived risk and moderated by a tight-loose culture. Specifically, perceived risk significantly mediates the effect of innovation loci on consumers' adoption of medical artificial intelligence. Loose and tight culture moderates the direct and mediating effects. These findings are important theoretical contributions to the artificial intelligence and healthcare literature. We also provide some practical implications to promote the future development of medical artificial intelligence products and to improve consumers' acceptance of medical artificial intelligence.

Research on the application of artificial intelligence generated AI technology in new media art

With the rapid development of information society and computer science, new media art has not made a qualitative leap in the past decade. The reason is that in big data and machine learning, the rapid development of artificial intelligence technology has brought computer science up to a higher level, while art is still stuck in aesthetics, acoustics, vision, psychology and other aspects, lack of logic, intelligence and integration. Therefore, the new media art needs the support of the computer technology, especially the new technology.In the design stage of new media art, technologies such as artificial intelligence, machine learning and big data mining are integrated to improve the molding speed of new media art through mathematical modeling.Through machine learning, quickly understand user experience and user needs, further optimize new media art works, use artificial intelligence AI technology to simulate new media art, and simulate the whole process of user experience and interaction is the main content of this paper.This makes new media art become a typical application in the field of AICG (artificial intelligence production content). Generative AI will greatly reduce the marginal cost of creation and knowledge work, greatly improve labor productivity and economic value, and realize the generation of new media art original content at one tenth of the cost and thousands times production speed.

The AI Galaxy: A Comparative Study of Cutting-Edge AI Technology

Abstract: The world of artificial intelligence (AI) has evolved into a vast array of technologies. The purpose of this paper is to explore a range of AI models and products, including GPT-4, GPT4-0613, GPT-3.5-Turbo, GPT-3.5-Turbo-0613, Claude-1, Claude-2, Claude-Instant-1, GPT-4-32K, GPT-4-32K-0613, GPT-3.5-Turbo-16K, GPT-3.5-Turbo-16K-0613, Claude-1-100K, Claude-1-100K, Claude-1-Instant-1-100K, Google BardAI, Microsoft Bing, and Perplexity. It will cover their capabilities, use cases, and what makes them unique in today's ever-expanding world of artificial intelligence. A comparison of these models and products will also be presented, highlighting the strengths and weaknesses of each.

Are you adopting artificial intelligence products? Social-demographic factors to explain customer acceptance

Are consumers accepting AI-based products? What are the socio-demographics influencing the adoption of these products? This study tests the potential users’ social-demographic characteristics that influence the relationship between innovation and AI-based products. The latter are robots (e.g. chatbots) and AI (e.g. recommendation systems, amongst others). A mixed methods approach is adopted using both qualitative and quantitative analysis with non-metrical multidimensional scaling (NMDS) to map opinions about digitally-intensive products, such as robots and AI, and the attitude towards innovation. The research uses data on the general population from the Spanish innovation barometer survey (N = 3,005). Findings show that individuals who have a negative attitude towards innovation have a negative opinion about robots and AI. As regards social-demographic dimensions, age and economic conditions moderate this effect, causing a more positive opinion towards digitally-intensive products amongst young people and individuals with a higher socio-economic level. These effects are increased by the moderating role of sex.

A value-oriented Artificial Intelligence-as-a-Service business plan using integrated tools and services

The latest developments in Artificial Intelligence (AI) are the focal point in increasing the performance of other technologies and the evolution of Industry 4.0. Considering the benefits of AI in today’s world, businesses must move towards using Integrated AI tools and services. This paper introduces a business model based on AI-as-a-Service (AIaaS), which provides an integrated bundle of AI products and services. The strategic approach, roadmap, and heuristic pricing model provided in this paper can be considered as a benchmark for AIaaS companies.

Follow-up of Artificial Intelligence Development and its Controlled Contribution to the Article: Step to the Authorship?

Follow-up of Artificial Intelligence Development and its Controlled Contribution to the Article: Step to the Authorship?

A descriptive study based on the comparison of ChatGPT and evidence-based neurosurgeons

ChatGPT is an artificial intelligence product developed by OpenAI. This study aims to investigate whether ChatGPT can respond in accordance with evidence-based medicine in neurosurgery. We generated 50 neurosurgical questions covering neurosurgical diseases. Each question was posed three times to GPT-3.5 and GPT-4.0. We also recruited three neurosurgeons with high, middle, and low seniority to respond to questions. The results were analyzed regarding ChatGPT's overall performance score, mean scores by the items' specialty classification, and question type. In conclusion, GPT-3.5's ability to respond in accordance with evidence-based medicine was comparable to that of neurosurgeons with low seniority, and GPT-4.0's ability was comparable to that of neurosurgeons with high seniority. Although ChatGPT is yet to be comparable to a neurosurgeon with high seniority, future upgrades could enhance its performance and abilities.

Performance of a commercially available artificial intelligence software for the detection of confirmed pulmonary nodules and masses in canine thoracic radiography.

Advancements in the field of artificial intelligence (AI) are modest in veterinary medicine relative to their substantial growth in human medicine. However, interest in this field is increasing, and commercially available veterinary AI products are already on the market. In this retrospective, diagnostic accuracy study, the accuracy of a commercially available convolutional neural network AI product (Vetology AI®) is assessed on 56 thoracic radiographic studies of pulmonary nodules and masses, as well as 32 control cases. Positive cases were confirmed to have pulmonary pathology consistent with a nodule/mass either by CT, cytology, or histopathology. The AI software detected pulmonary nodules/masses in 31 of 56 confirmed cases and correctly classified 30 of 32 control cases. The AI model accuracy is 69.3%, balanced accuracy 74.6%, F1-score 0.7, sensitivity 55.4%, and specificity 93.75%. Building on these results, both the current clinical relevance of AI and how veterinarians can be expected to use available commercial products are discussed.

Exploring the Opportunities and Challenges of Developing Large AI Models and their Commercialization

This article explores the evolution and impact of large models in artificial intelligence (AI), with a focus on their role in advancing fields like natural language processing and image recognition. It categorizes this evolution into three stages: deep learning revival, big data and distributed computing, and self-supervised learning. The capabilities of large-scale models, especially in generating AI content, are discussed, along with the role of small models in personalized enterprise requirements and resource-limited environments. Discusses the development of large AI models and the opportunities they bring, as well as the importance of considering user needs when transitioning from a model to a product. The commercialization of AI products is also explored, with attention paid to the details of this process. The future of AI is predicted to be a dynamic balance between large and small models, depending on task requirements and resource limitations. The article concludes with a discussion on the commercialization of AI products, emphasizing the importance of data, algorithms, user-centric design, and viable business models.

The Influence of Anthropomorphic Appearance of Artificial Intelligence Products on Consumer Behavior

The Influence of Anthropomorphic Appearance of Artificial Intelligence Products on Consumer Behavior

Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022.

To map the clinical use of CE-marked artificial intelligence (AI)-based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022. Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis. The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings. The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties. The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products. • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence-based software; however, major obstacles to adoption remain the costs and IT-related difficulties.

Artificial Intelligence and Article Writing

Dear Editors, I was very interested in your editorial [1]. Technological advancements are so rapid that humans are unable to keep up. And we eagerly await the outcomes of technological advancement. The study of artificial intelligence is one of the topics that has recently gained popularity. If someone had predicted a hundred years ago, "A technology will come that will detect the commands you receive, act accordingly, and write scientific articles," we would laugh at her. Many artificial intelligence dreams that appear weird to us now, I believe, will come true shortly. The Generative Pre-Trained Transformer 3 (GPT3) was developed a few years ago, in line with improvements in artificial intelligence. With this model, artificial intelligence was used to generate content that resembled human-written documents. A more advanced version of ChatGPT was produced a few years later. In March 2023, the most recent GPT4 version was launched. Errors in article writing have been reduced using this and comparable applications such as artificial intelligence. In fact, it has become nearly impossible to detect the difference between publications written by scientists and articles written by artificial intelligence. Some journals have begun to accept artificial intelligence apps as co-authors [2]. Article authoring is made considerably easier by artificial intelligence. In terms of time savings, using artificial intelligence products such as ChatGPT and GPT 4 may make sense. However, there are some risks associated with this circumstance. For example, because disseminating personal data is a felony, authors may feel compelled to conceal some information in order to preserve personal data. Artificial intelligence programs can readily perpetrate crimes involving personal data by releasing secret facts. This situation presents numerous legal issues. Furthermore, the language to be used in article writing may alter depending on the topic of study. In this instance, it is vital to go check the words chosen by artificial intelligence programs in the article. As a result, my recommendation is to use tools such as ChatGPT, GPT 4, or similar basic jobs once the user has written the article themselves. Leaving all of the work to these apps may cause more harm than good. Kind Regards